Assessing the Genomic Landscape of <i>Salmonella enterica</i> Isolated From Cattle Faeces on a Nigerian Farm
Bibliographic record
Abstract
Antibiotic resistance is a global menace, particularly in low- and middle-income countries where antimicrobial resistance (AMR) in zoonotic pathogens like Salmonella is on the rise. This study investigates the phenotypic and genotypic AMR in Salmonella enterica. isolated from cattle faeces collected by faecal grab method on a Nigerian dairy farm. Salmonella enterica was cultured from the faecal samples of 138 individual cattle at the University of Ibadan dairy farm, with identification done through MALDI-TOF-MS, genus-specific PCR, and Microbact 24E. The minimum inhibitory concentration (MIC) of selected antibiotics was determined by Vitek 2 compact system. Whole genome sequencing was conducted on eighteen isolates that met pre-sequencing quality standards, utilizing the Illumina HiSeq platform. Sequence types and AMR genes were determined using publicly available tools. Interestingly, all isolates showed 100% phenotypic susceptibility to the tested antibiotics. Notably, several rare Salmonella enterica serovars were identified among the sequenced strains; Koketime (n = 2), Hadar (n = 3), Banalia|Tounouma (n = 10), Hermannswerder (n = 2), and Chomedey|Glostrup (n = 1). While most of the sequenced Salmonella enterica strains (15 out of 18) lacked AMR genes besides efflux transporter gene, a strain of Chomedey|Glostrup serovar exhibited genes associated with reduced susceptibility to aminoglycosides (aph(3')-lb, aph(6)-Id), quinolones (qnrB), sulphonamides (sul2), and tetracycline (tet(A)), while Koketime strains possessed fosfomycin resistance genes (fosA7) besides the efflux genes. The absence of phenotypic and genotypic AMR in most of the isolates highlights the possibility that AMR could be controlled in livestocks in developing countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".